• Computer Science
  • Published in AIIDE 2014

Hierarchical Adversarial Search Applied to Real-Time Strategy Games

@inproceedings{Stnescu2014HierarchicalAS,
  title={Hierarchical Adversarial Search Applied to Real-Time Strategy Games},
  author={Marius Marinel Stănescu and Nicolas A. Barriga and Michael Buro},
  booktitle={AIIDE},
  year={2014}
}
Real-Time Strategy (RTS) video games have proven to be a very challenging application area for artificial intelligence research. Existing AI solutions are limited by vast state and action spaces and real-time constraints. Most implementations efficiently tackle various tactical or strategic sub-problems, but there is no single algorithm fast enough to be successfully applied to big problem sets (such as a complete instance of the StarCraft RTS game). This paper presents a hierarchical… CONTINUE READING

Citations

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Search, Abstractions and Learning in Real-Time Strategy Games

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A Review of Computational Intelligence for StarCraft AI

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Tabular Reinforcement Learning in Real-Time Strategy Games via Options

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μCCG, a CCG-based Game-Playing Agent for μRTS

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